Papers by Patrick Kahardipraja
When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality (2024.acl-long)
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| Challenge: | In incremental models, one interpretation is possible, but models that can revise can do so if the ambiguity is resolved. |
| Approach: | They propose an interpretable way to analyse incremental states in a bidirectional way . they propose to use a model that can update internal states to reflect the garden path effect . |
| Outcome: | The proposed model shows that it can perform revisions and recover if the label is incorrect. |
Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU (2021.emnlp-main)
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| Challenge: | Recent work attempts to apply incremental processing to NLUs but this is computationally expensive and does not scale efficiently for long sequences. |
| Approach: | They propose to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. |
| Outcome: | The proposed model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restart-incrementality, at the cost of part of the non-incremental quality. |
FADE: Why Bad Descriptions Happen to Good Features (2025.findings-acl)
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Bruno Puri, Aakriti Jain, Elena Golimblevskaia, Patrick Kahardipraja, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
| Challenge: | Recent advances in mechanistic interpretability have highlighted the potential of automating interpretability pipelines in analyzing the latent representations within LLMs. |
| Approach: | They propose a framework for automatically evaluating feature-to-description alignment that measures alignment across four key metrics and quantifies the causes of misalignment. |
| Outcome: | The proposed framework evaluates alignment across four key metrics and quantifies the causes of misalignment between features and descriptions. |
TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model (2023.findings-acl)
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| Challenge: | Recent approaches for incremental processing use RNNs or Transformers, which consume whole sequences and are by nature non-incremental. |
| Approach: | They propose a two-pass model for AdaPtIve Revision to obtain an incremental supervision signal for learning an adaptive revision policy. |
| Outcome: | The proposed model has better incremental performance and faster inference speed compared to restart-incremental Transformers while showing little degradation on full sequences. |